more fixes

examples/tutorial/xvla/inference.py

@@ -1,15 +1,13 @@
-from transformers import AutoModel, AutoProcessor
-import json_numpy
 import numpy as np
 import torch
 from PIL import Image
+from transformers import AutoModel, AutoProcessor
 
-model = AutoModel.from_pretrained(
-    "2toINF/X-VLA-WidowX",
-    trust_remote_code=True
-)
+model = AutoModel.from_pretrained("2toINF/X-VLA-WidowX", trust_remote_code=True)
 
 processor = AutoProcessor.from_pretrained("2toINF/X-VLA-WidowX", trust_remote_code=True)
+
+
 # append 3 random image to a list
 def make_random_pil_images(num_images=3, H=480, W=640):
     images = []
@@ -20,6 +18,7 @@ def make_random_pil_images(num_images=3, H=480, W=640):
         images.append(img)
     return images
 
+
 # Example:
 images = make_random_pil_images()
 language_instruction = "This is a random image"
@@ -29,23 +28,27 @@ if not {"input_ids", "image_input", "image_mask"}.issubset(inputs):
     raise ValueError("Processor did not return the expected keys.")
 
 proprio = torch.randn(1, 20)
-domain_id = torch.tensor([int(0)], dtype=torch.long)
+domain_id = torch.tensor([0], dtype=torch.long)
 
 # Align to model's device/dtype
 device = model.device
 dtype = next(model.parameters()).dtype
 
+
 def to_model(t: torch.Tensor) -> torch.Tensor:
     if not isinstance(t, torch.Tensor):
         t = torch.as_tensor(t)
     # cast floats to model dtype, keep integral/bool as-is
     return t.to(device=device, dtype=dtype) if t.is_floating_point() else t.to(device=device)
 
+
 inputs = {k: to_model(v) for k, v in inputs.items()}
-inputs.update({
-    "proprio": to_model(proprio),
-    "domain_id": domain_id.to(device),
-})
+inputs.update(
+    {
+        "proprio": to_model(proprio),
+        "domain_id": domain_id.to(device),
+    }
+)
 
 # Inference